Method for acquisition and distribution of product price information

ABSTRACT

A method for determining product price information, includes capturing a data signal including image data, voice data, and/or keypad input data, the captured data signal including information related to a price of a product, to a point of retail of the product and to a type of the product; determining metadata including a geographic position of the capturing and a time of the capturing; determining a piece of product price information including the type of the product, the price of the product, and the point of retail by analyzing the captured data signal; determining credibility data of the piece of product price information; and including the piece of product price information, the metadata and the respective credibility data into a database.

BACKGROUND

Field of the Disclosure

An embodiment of the disclosure relates to a method of determiningproduct price information. Further embodiments of the disclosure relateto a mobile device, a server, a system, a computer program and anon-transitory computer-readable medium for determining product priceinformation.

Description of Related Art

The “background” description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description which may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentinvention.

Presently, methods and devices for determining and comparing productprice information, such as e.g. fuel price information, rely on manualdata acquisition, which to the largest extent is being performedvoluntarily by participants manually entering and assigning productprice information, e.g. a fuel price, to a specific retailer. Thefrequency with which pricing information is updated is often low, andthe accuracy thus rather poor.

Accordingly, there is a need for a method, system and devices allowingdetermining and comparing product price information with a high accuracyand reliability, thereby leading to a good user acceptance.

This object is solved by a method, a mobile device, a server, a system,a computer program and a non-transitory computer-readable mediumaccording to the independent claims.

SUMMARY

A method for determining product price information includes capturing adata signal including image data, voice data and/or keypad input data,the captured data signal including information related to a price ofproduct, to a point of retail of the product and to a type of theproduct, determining metadata including a geographic position of thecapturing and a time of the capturing, determining a piece of productprice information including the type of the product, the price of theproduct, and the point of retail by analyzing the captured data signal,determining credibility data of the piece of product price information,and including the piece of product price information, the metadata andthe respective credibility information into a database.

A mobile device includes a capturing device including a still imagecamera, a video recorder, a microphone and/or a keypad adapted tocapture a data signal relating to at least one of a price of a product,a type of the product, a point of retail, a document of retail and voiceinformation, a clock for determining a time of capturing, a positiondetection device adapted to detect a geographic position at the time ofcapturing, and a communication interface adapted to transmit, to aserver over a network, at least one of the captured data signal togetherwith metadata including the time of capturing and the geographicposition of capturing and a piece of product price information derivedfrom the captured data signal together with the metadata.

A server includes a communication interface adapted to receive inputdata including information related to a type of a product, to a price ofthe product, and to a point of retail, the input data further includingmetadata, the metadata including a time of capturing of a data signalfrom which data signal the information is derived, and a geographicposition of the capturing of the data signal, a storage accessor adaptedto access a local and/or remote storage, further adapted to transmit apiece of product price information including the type of the product,the price of the product, and the point of retail together with themetadata to the storage.

A system for determining product price information includes a pluralityof mobile devices as described above, and at least one server asdescribed above, wherein credibility data of the piece of product priceinformation is determined at one of the plurality of the mobile devicesand/or at the server.

A computer program includes computer-program instructions, which whenexecuted by a computer, cause the computer to perform method asdescribed in the above.

A non-transitory computer-readable medium includes the computer programdescribed above.

The foregoing paragraphs have been provided by general introduction, andare not intended to limit the scope of the following claims. Thedescribed embodiments, together with the further advantages, will bebest understood by reference to the following detailed description takenin conjunction with the accompanying drawings. The elements of thedrawings are not necessarily to scale relative to each other.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and of many of theintended advantages thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings, wherein:

FIG. 1 illustrates a method for determining product price information;

FIG. 2 illustrates a method for assigning credibility data to arespective piece of product price information;

FIG. 3 illustrates a method for determining fuel price information byaggregating, for at least one type of fuel and/or for at least one pointof retail, pieces of fuel price information and respective credibilitydata;

FIG. 4a illustrates image date of a customer receipt includinginformation related to a retailer, a point of retail, a type of aproduct, a quantity of the product bought and pricing information;

FIG. 4b illustrates image data of a price sign at a fuel station;

FIG. 4c illustrates image data of price information at a fuel pump;

FIG. 5a illustrates a system for determining product price information;

FIG. 5b illustrates an embodiment of any of the devices included in thesystem of FIG. 5 a;

FIG. 6 illustrates the swarm principle of collecting pieces of productprice information to be aggregated and provided to a community of users;

FIG. 7 illustrates a display of average expenses due to productconsumption provided to a user as a reward for gathering product priceinformation; and

FIG. 8 illustrates a further embodiment of the method for product pricedetermination.

DESCRIPTION OF THE EMBODIMENTS

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views, FIG. 1illustrates a method for determining product price information.

At 100, a data signal including image data, voice data and/or keypadinput data is captured. The data signal may for example include a stillimage, a moving image such as a sequence of video frames or video data.The still or moving image data may be captured by a camera including anoptical lens system and a storage for storing the image data. The datasignal may further include voice data, such as a speech uttered by auser, and/or audio data captured e.g. by a microphone. Further, the datasignal may include keypad input data, i.e. data representing and/orencoding a sequence of inputs entered by a user e.g. by touching keysprovided on a keyboard and/or on a touch pad, i.e. touch sensitivedisplay device.

The device capturing the data signal may thus include a camera for stillor moving images, a microphone, a keyboard, keypad and/ortouch-sensitive display device, and further a storage for storing thecaptured data signal. It may, for example, be a mobile device carried bythe user, such as mobile phone or smartphone, or a device integrated ina mobile system, such as a camera system or microphone included in a cardriven by the user, or may also be a stationary device such as a fixedlyinstalled still image or video camera, optionally including amicrophone.

The captured data signal includes information related to a price of aproduct, to a point of retail of the product, and to a type of theproduct. This information may be included e.g. as a part of the capturedimage data, as a part of the keypad input data, and/or as a part of thecaptured voice data. The information may e.g. be included in a semanticcontent recognizable from the image data and/or in a sematic content ofthe voice data, e.g. of a speech recorded by the microphone, and or maybe encoded in a character string entered by the keypad.

The capturing of the data signal may be explicitly triggered by theuser. For example, the user of a mobile device may explicitly captureimages of the point of retail of the product, e.g. of a sign publishingan offer of the product, and/or of a customer receipt when having boughtthe product. For example, the data signal may include an image of a fuelpump at which a price of a fuel is indicated.

Alternatively or in addition, the user may enter the data signalexplicitly as a voice input, e.g. speech input into the microphone.Entering a voice input may be convenient, since it requires very littleinteraction with the capturing device, e.g. no or very littleinteraction with keys of the device. Thus, a speech input mayconveniently be entered by a user e.g. passing by a gas station in hiscar, checking the fuel price offers by his eyesight. While it may bedifficult for the user to capture image data of the fuel price offers,e.g. since having to focus at the offer signs with his camera, it may beeasy for him to enter the information as a voice input into a dictationdevice e.g. integrated in an arbitrary device, such as a smartphone, aportable computer or a car communication system. For example, the usermay activate the dictation device by any action that may be required foractivating the device, e.g. by uttering a speech command into thedevice, by lifting the device, by tilting the device and/or by pressinga “dictation” button. By any of these actions, the device may beactivated, even if it has been idle at this time, e.g. in a stand-bymode. Then, the user may utter and capture a speech input summarizingthe offers. While capturing the speech input, the user may stay in touchwith his driving equipment/steering wheel, as often required by theauthorities of specific countries, which allows him to safely continuehis road.

The capturing of the data signal may further happen automatically, i.e.without a triggering event expressly entered by the user. For example,if a camera of the mobile device or of a car camera system recognizesimage data of a product offer, a product retailer, a service station orpoint of retail of fuel, the image data may automatically be recorded asthe data signal. Thus, the capturing of the data signal may happen in afully automatic manner, i.e. without explicit user interaction. The usermay or may not be informed about the capturing.

The user may enter and/or capture the data signal, may the recording beimplicit or explicit, since he wishes to participate in a community ofusers sharing product price information, e.g. fuel price information.

The product may be any kind of product of which pricing information maybe of interest for a user, e.g. a product which is available forconsumers at various point of retail or points of service having e.g.differing prices. For example, the product may be fuel, food or ediblegood, such as butter, beer, meet or orange juice, a basket of availableor personally predetermined goods required by a user, such as histypical Saturday shopping basket, an electronic device such as atelevision set, a service such as car wash, a rental price e.g. forrenting a car, a room rate at a hotel/motel and/or a price for hardwareat a building supplies store, i.e. home improvement store. Thus, pricinginformation may be of interest for consumers when determining whichpoint of retail to address, e.g. to go to.

The price of the product may be a price of a unit, e.g. a price to bepaid for a predetermined measure of capacity, such as 500 g butter or aliter or a gallon of fuel, or may be a total price for a single piece ofthe product or for an overall quantity to be sold.

The retailer of the product may for example be a grocery, a hotel and/ora service station having one or several fuel pumps. The retailer mayfurther have advertising signs promoting prices of different products,e.g. different types of fuel. The types of the fuel may describedifferent compositions, e.g. according to a custom of a specificcountry. For example, the type of fuel may include normal fuel, dieselfuel, unleaded fuel and other types of petrol.

At 102, metadata is determined including a geographic position of thecapturing and a time of the capturing.

For example, the capturing device may include a position sensor such asa GPS (Global Positioning System) sensor for determining a geographicposition, i.e. a location of the device essentially at the moment ofcapturing. The geographic position may also be determined in anothermanner, e.g. by evaluating a cell of a cell-based network such as acell-based telecommunications network in which the capturing device islocated at the time of capturing. Further, is the capturing device isfixedly installed, the geographic position may be predetermined and e.g.stored in a storage of the capturing device.

Further, the device may include a clock and/or means for accessing aclock at another device for determining essentially the time ofcapturing.

At 104, a piece of product price information is determined. The piece ofproduct price information includes the type of the product, e.g. fueltype, the price of the product, e.g. fuel price, and the point ofretail, e.g. an offering service station. This information is determinedby analyzing the captured data signal.

The analyzing of the captured data signal may be performed by any methodof signal processing, e.g. adapted for image data processing and/orvoice data processing. These methods may include e.g. optical characterrecognition, image pattern recognition, speech recognition, voice dataanalysis, audio feature analysis and/or data signal enhancement, such asenhancement of a sharpness or a contrast, or the like.

The analysis of the captured data signal may be performed at the device,e.g. at the mobile device. This allows an instantaneous determination ofthe piece of product price information using the processing capabilitiesof the capturing device.

Alternatively or in addition, the analysis may also be performed at aserver, e.g. forming part of a cloud computing environment accessiblefrom the capturing device, providing processing capabilities to theparticipants of the embodiment of the method. In such a configuration,processing capabilities at the capturing device may be kept low, andextensive methods for data analysis may be used at the server, allowinga high accuracy of recognition.

At 106, credibility data of the piece of product price information isdetermined. The credibility data may reflect a reliability and/or atrustworthiness of the respective piece of product information.

For example, the credibility data may be determined based on a set offactors contributing to the credibility of the conveyed priceinformation. For example, a “trust score” of the piece of product priceinformation, e.g. fuel price information, may be determined. The trustscore may comprise individual factors expressing credibility ortrustworthiness, which individual factors can be weighted e.g. in alinear or nonlinear fashion to derive an overall score. For example,each of the factors may be represented by a value between 0 and 1, where1 reflects total trust worthiness, and 0 reflects that the data is nottrustworthy at all.

The credibility data may be regarded as a hypothesis for the probabilitythat the piece of product price information is correct at the time ofcapturing, e.g. that it reflects the correct price of the product typeat the corresponding retailer. Further details for determining thecredibility data will be discussed below.

At 108, the piece of product price information, the metadata and therespective credibility data is included into a database. This databasemay be managed and stored at a central server or at storage capabilitiesprovided in the cloud environment. For example, the database may bestored in a distributed manner at several servers, wherein each of theseveral servers is located in a certain city and stores pieces ofproduct price information of retailers located in the city.

The above-discussed steps 100 to 108 thus allow obtaining real-timeprice information according to the “swarm principle”. Hence, if asignificant number of users participate, quasi real-time priceinformation can be gathered and stored through the employment of manydifferent capturing devices providing the pieces of product priceinformation as derived from the captured data signals in a collaborativefashion.

At 110, it is shown that a step of aggregating, for at least one of aplurality of types of product and/or for at least one of a plurality ofpoints of retail, pieces of product price information and the respectivecredibility data in the database might be used. Step 110 may for examplebe used if a plurality of pieces of product price information for a typeof a product and a point of retail are available.

During aggregation, an overall price of the type of the product at thepoint of retail may be determined. This aggregation may be performedaccording to any aggregation method known e.g. from statistics. Forexample, a mean price indicated by the pieces of product priceinformation may be determined. Further, pieces of product priceinformation showing a widely deviating price may be excluded from theaggregation, thus yielding a truncated mean. Further, the aggregationmay be based on the respective credibility data. For example, pieces ofproduct price information having a low credibility may be excluded fromthe aggregation. The pieces of product price information may beweighted, e.g. in a weighted sum, according to their respectivecredibility.

At optional step 112, for at least one of the plurality of types ofproduct and/or for at least one of the plurality of points of retail,product price information may be estimated, and credibility informationof the estimated product price information may be estimated based on aplurality of pieces of product price information and respectivecredibility data. Thus, an overall estimated price of the type of theproduct at the point of retail is determined. Further, the respectivecredibility information reflects the trustworthiness of the overallestimated price of the product.

For estimation, any estimation method e.g. known from statistics may beused. For example, a mean value of the product prices indicated in thepieces of product price information, optionally weighted with therespective credibility data, may be calculated. As for the aggregation,also during the estimation, pieces of product price information having alow credibility may be excluded, thereby leading to a truncated methodfor estimation (e.g. truncated weighted mean determination). Further,the estimation may also be performed by using a neural network. Theestimation method, e.g. the neural network, might for example take intoaccount further factors, such as product price information fromneighbouring points of retail, e.g. neighbouring service stations of thesame retailer.

In addition to the pieces of product price information, the estimationmay be based on further information, e.g. pricing information providedfrom other information sources, such as prices published by the retailerhimself. Thus, a thorough estimation of the price of the type of productat the point of retail, taking into account the pieces of product priceinformation, the credibility data and further sources of information maybe performed. Estimation results may be included into the database.

At 114, it is illustrated that upon a request for product priceinformation received at the database, the credibility information istransmitted together with the product price information. For example,users requesting information on product prices, e.g. requesting anindication of points of retail having a low price for a required type ofproduct in an area surrounding the user, do not only receive a list ofretailers and the corresponding estimated prices for the product, butfurther receive credibility information for each of the estimatedprices, indicating an overall credibility of the estimated price.Accordingly a user can determine or estimate his own personal risk whendriving to a point of retail with the aim of buying the product at theestimated price, that the driving was not worth the effort and the fuelused for this drive has been wasted since the estimated price wasincorrect and/or is not available.

For example, if a very low price of the product at a specific point ofretail is reported to a user, the user may notice from a low credibilitydata that the pricing information may not be reliable, e.g. sincerelying on data, e.g. pieces of product price information, having a poorquality indicated by the respective credibility data.

The method therefore not only provides highly accurate real-time data,but also indicates the credibility of the provided pricing information.

In FIG. 2, determination of the credibility data of a piece of productprice information is illustrated, as performed at step 106. Thecredibility data may be obtained from a function 200 operating onvarious inputs allowing to determine, e.g. to estimate, the credibility,i.e. trustworthiness of a piece of product price information.

For example, as an input to function 200, the time of capturing the datasignal is provided at 202-1. If the time of capturing lies widely in thepast, e.g. before a predetermined accepted transmission period of e.g.two hours ago, the corresponding piece of product price information maybe considered as having a low credibility. Thus, the metadata mayindicate that the piece of product price information is outdated.Further, if the time of capturing lies in the future, it may be supposedthat an error made have occurred, such that the piece of product priceinformation may have very low credibility, e.g. near or equal to zero.

Further, function 200 may rely on a time of submission, as illustratedat 202-2. For example, if the time of capturing and the time ofsubmission deviate, this may be an indication for an error and thus fora piece of potentially erroneous product price information.Correspondingly, function 200 may assign credibility data indicating alow credibility.

Further, function 200 may rely on a signal quality, as illustrated at202-3. For example, if the signal quality, e.g. image quality, is lowdue to a noisy signal, the corresponding pricing information determinedfrom the data signal may be erroneous, and the credibility data may beset low by function 200.

Further, as illustrated at 202-4, other pieces of product priceinformation may be analyzed. The credibility of the piece of productprice information may be high if a high number of other pieces ofproduct price information indicating the same price for the same productat the same point of retail are present.

On the other hand, as illustrated at 202-5, the credibility of a pieceof product price information may be low in the presence of a largenumber of pieces of product price information indicating deviatingprices.

Further, as illustrated at 202-6, the piece of product price informationmay be matched with a reference price, e.g. a price information conveyedby the retailer e.g. on an internet site (reference site). If the pieceof product price information deviates from the reference site, itscredibility may be low.

Further, as shown at 202-7, mood information may be derived from thedata signal. For example, it may be determined whether the user islaughing or shouting. In these cases, the credibility of the respectivepiece of product price information may be low.

Further, as illustrated at 202-8, further information may be derivedfrom the data signal and may be matched. For example, if the data signalincludes image data captured at the point of retail, it may be checkedwhether a colour present in the image data matches to a colour used bythe retailer for marking his points of retail. The presence of thematching colour may be an indication for a high credibility, inparticular regarding the point of retail.

Further, as illustrated at 202-9, also a pattern present in the captureddata signal, i.e. image data, may be matched with a predefined patternused by the retailer for marking his points of retail. For example, itmay be known that a specific retailer marks his points of retail with astar. If a corresponding star is present in the image data, this may bean indication that the piece of product price information, and inparticular the point of retail, is encoded correctly in the piece ofproduct price information.

Further, as shown at 202-10, function 200 may also depend on asubmission history e.g. of a specific user or of the capturing device.For example, credibility data of another piece of product priceinformation captured previously at a corresponding device, e.g. the samedevice or a device of the same user, may be used for determining thecredibility data. For example, if a user of a smartphone has previouslytransmitted erroneous pieces of product price information at severaltimes, the credibility of the newly transmitted piece of product priceinformation may be estimated low.

As shown at 202-11, function 200 may also depend on personal credibilitydata of the user. For example, personal credibility data may be obtainedby analysing the credibility data of pieces of product price informationthat have been provided by the user in the past. Further, personalcredibility data may also be obtained by accessing further informationsources, e.g. servers or services accessible via internet. For example,web sites providing social networks or providing market places mayevaluate the credibility of their users, e.g. when acting either ascustomers or as retailers, e.g. by evaluating feedback given to theirtransactions, payments or the like, thus yielding personal credibilitydata.

Still further, as shown at 202-12, the product price information mayalso matched with further information, e.g. price information providedby and/or determined for e.g. neighbouring points of retail of the sameretailer.

By function 200, any combination of the above individual factorsillustrated by 202-1 to 202-10 may be calculated. For example, function200 may be realized by a mathematical function, by a set of rulesencoded e.g. in a expert system or the like, or by a neural network.

As a result, a probability of correctness of a piece of product priceinformation may be determined and output as the credibility data at 204.

Further, credibility information of an aggregated or estimated price ofa product/a type of product, e.g. a type of fuel, at a point of retailmay be determined in a corresponding manner, e.g. based on function 200.In this case, the probability is also referred to as the credibilityinformation of the product price information.

The credibility information may be transmitted to a user requestingproduct price information e.g. together with estimated price informationfor a type of product at a point of retail. The user, receiving suchinformation, may estimate his own risk regarding the correctness of thepricing information.

The credibility information may be transferred and displayed to the userin various ways, e.g. as a numeric value or coded by colour, e.g. greencolour if the credibility of the estimated product price is high, yellowif the credibility of the estimated product price is medium and red ifthe credibility of the estimated product price is low.

FIG. 3 illustrates the aggregation and estimation of fuel prices, as anexample for the aggregation and estimation of product prices ofarbitrary products, as well as the aggregation of credibilityinformation in a geographic area 300 with points of retail 1 to 4denoted by 304 a, 304 b, 304 c and 304 d.

In the example depicted, it is assumed that for a specific type of fuelat point of retail 1 304 a, pieces of a fuel price information P_(1,1)to P_(1,n) with respective credibility data C_(1,1) to C_(1,n) areavailable. An overall price for the specific fuel type at point ofretail 1 304 a is then determined by P1 as the mean of the pieces offuel price information. Accordingly, also the overall credibility C1 ofthe estimated fuel price P1 is determined by the mean of the respectivepieces of credibility data. Corresponding aggregated pricing informationis also determined for the points of retail 2 to 4 304 b, . . . , 304 d.

It should be noted that the aggregated and/or estimated fuel price andthe credibility information may also be determined by any other methodof aggregation, estimation or calculation, e.g. by a weighted mean offuel prices, e.g. weighted by the respective credibility data.

If a person 306 requires pricing information regarding a type of fuel ina surrounding environment 308, the aggregated or estimated priceinformation P1, P3 and P4 of the points of retail 304 a, 304 c and 304 dlocated in surrounding area 308 may be transmitted to person 306together with the corresponding credibility information C1, C3 and C4.Thus, person 306 may decide to which point of retail to go, taking intoconsideration the risk that the estimated price information may not becorrect.

FIGS. 4a to 4c show image data related to a price of fuel, a retailer ofthe fuel and a type of the fuel as illustrations of data signals relatedto product price information.

FIG. 4a shows a captured image of a customer receipt 400 printed at astation XXX, upon which the address of the station is printed, and uponwhich the product type, the quantity bought and the overall price isindicated. This information may be analyzed from the captured datasignal, i.e. from the image data. Thereby, a piece of product priceinformation including the product type, the price and the point ofretail may be derived. In the case depicted the product may be fuel or adifferent type of product, such as oil, milk or wine.

FIG. 4b illustrates an image captured by a car approaching a point ofretail, upon which an advertising sign 402 indicating the types of fueland the corresponding prices is visible. Further, a star forming apattern used by the retailer for marking his point of retail isdepicted. By analyzing the corresponding image data, three pieces offuel type information including each of the three fuel types and thecorresponding fuel price together with the point of retail may bederived. Further, credibility data may be determined based on analysisof the pattern, i.e. the star, indicated on the advertising sign. If, atthe geographic location of capturing, a retailer using a star as patternfor marking his points of retail is known to be located, the image datamay have a high credibility.

In FIG. 4c , image data 404 captured at a fuel pump is illustrated. Froman analysis of the image data, a price of the fuel per measure ofquantity may be calculated and thus included in a piece of fuel priceinformation. Further, at the fuel pump, the pattern of the star isvisible, thus allowing determining the credibility data, e.g. bychecking whether at the geographic location of capturing a retailerusing the star as a pattern for marking his points of retail is located.

In FIG. 5a , a system for determining product price informationincluding capturing devices and a server is illustrated.

It should be noted that the illustration only shows an embodiment of thesystem, and that the system may also include a cloud environment, e.g. acommunity cloud or public cloud. It is thus to be understood that thecapturing devices as well as the server may be coupled to a cloudcomputing environment, and that any of the method steps, i.e. any of thetasks to be performed, may be performed by any of the devices includedin the cloud computing environment.

Cloud computing is to be understood as a model for enabling ubiquitous,convenient, on-demand network access to a shared pool of configurablecomputing resources, e.g. networks, servers, storages, applications, andservices, that can be rapidly provisioned and released with minimalmanagement effort and/or with minimal or no service provider interactionat all.

Thus, any of the tasks and method steps of embodiments of the methoddescribed herein may be performed by any component of the cloud, e.g. byrapid assignment and release. Thus, the applications running in thecloud environment, i.e. provided by the cloud infrastructure, may beprovided to the consumers, i.e. users of the capturing devices and usersrequesting product price information, by rapid assignment and release.The applications may be accessible from the various devices for examplethrough a thin client interface such as a web browser or a programinterface.

It should be understood that for the consumer, i.e. user of the variousdevices, it is not necessary, and further perhaps not possible, tomanage or control the underlying cloud infrastructure including network,servers, operating systems, storage, or even individual applicationcapabilities. However, limited user-specified application configurationsettings may be possible.

It should further be noted that the system may be designed as acommunity cloud providing the cloud infrastructure for exclusive use bya specific community of consumers having shared concerns, i.e. the wishto contribute to the determination of product price information and tobenefit from the estimated pricing information. The cloud infrastructuremay be owned, managed, and operated by one or more of the organizers ofthe community, by a third party, or by some combination of them, and itmay exist on or off premises.

Further, the cloud infrastructure may also be organized as a publiccloud provisioned for open use by the general public. It may be owned,managed and operated by a business, academic or government organization,or some combination of them. It may exist on the premises of the cloudprovider.

The cloud infrastructure may also be a hybrid cloud, i.e. a compositionof two or more distinct cloud infrastructures (private, community, orpublic) that remain unique entities, but are bound together bystandardized and/or proprietary technology that enables data andapplication portability.

It should further be noted that all definitions of the terms used hereinare to be reflected in the cloud computing environment. Accordingly, astretched interpretation of the terms in the sense of the dynamicattribution of tasks to different cloud structures is possible.

Concluding, any description related to an interaction between thedevices shown in FIG. 5a and any assignment of method steps e.g. asshown in FIG. 1 may be subjected to a stretched interpretation and thusmay be dynamically shifted in the cloud environment.

A mobile device 500 includes a capturing device 502 including a stillimage camera, a video recorder, a microphone, and/or a keypad forcapturing keypad input data. Capturing device 502 may be adapted tocapture data signal relating to at least one of a product price, aproduct type, a point of retail, a document of retail, voice data and/orkeypad input data. Mobile device 500 may further include a clock 504 fordetermining a time of capturing and a position detection device 506adapted to detect a geographic position at the time of capturing. Forexample, a GPS sensor may be included for determining a locationdepending on the global positioning system. The device may furtherinclude e.g. a display 508, and one or more keys 510 for operating thedevice, e.g. for starting the capturing.

Mobile device 500, as well as any other mobile device discussed herein,may be adapted for use in a system for determining product priceinformation, the system including a processor, the processor beingadapted to determine credibility data of a piece of product priceinformation. The processor may be located in the mobile device, in aserver of the system or in an any other component of the system, e.g. inthe cloud environment.

Still further, mobile device 500 may include a communication interface512 adapted to transmit, to a server over a network 513 e.g. located inthe cloud computing environment, at least one of the captured datasignal together with metadata including the time of capturing and thegeographic position of capturing and a piece of product priceinformation derived from the captured data signal together with themetadata. Communication interface 512 may be any kind of communicationport, such as e.g. an Ethernet port or an interface for wirelesscommunication via a telecommunications network such as an UTMS or LTEnetwork.

As a further device in the cloud computing environment, a car 514 havinga communication interface 516 for communicating via network 513 with thecloud computing environment is illustrated. Car 514 includes a camera518 for capturing still image data or sequences of video frames. Camera518 may be used as device for capturing the data signal.

As a further component of the cloud, a stationary camera 520 beingmounted in a fixed position at a service station so as to capture imagesof a fuel pump 522 is illustrated. Stationary camera 520 has acommunication interface 524 for communicating e.g. via networks 513 withthe cloud computing environment. It should be noted that instead ofbeing located at a fuel pump, the camera may also be mounted at adifferent location or retailer, e.g. so as to capture image data of anadvertising sign announcing product prices or at a shelf from whichproducts to be sold are available in a point of retail.

Any of the devices 500, 514 and 520 may be used as the capturing devicein the method illustrated in FIG. 1, e.g. for carrying out any one ofthe steps 100 to 108. However, it is possible that devices 500, 514 and520 only perform a part of the steps, e.g. only the capturing step 100,and possibly also the determination of the metadata at step 102. In thiscase, steps 104 to 108 may be performed elsewhere in the cloud computingenvironment, e.g. at specific servers being adapted e.g. for determiningthe piece of product price information, for determining the credibilitydata and/or for including the piece of product price information, themetadata and the respective credibility data into the database.

Further, any of the devices 500, 514 and 520 may include a processoradapted to determine a piece of product price information including theproduct type, the product price and the point of retail by analyzing thecaptured data signal. Further, the corresponding communicationinterfaces 512, 516 and 524 may be adapted to transmit the determinedpiece of product price information together with the metadata to theserver over network 513, e.g. to the cloud computing environment. Thus,step 104 may be performed e.g. by the respective processor of at leastone of the devices 500, 514 and 520.

Further, in any of the devices 500, 514 and 520, the processor may beadapted to determine the credibility data of the piece of product priceinformation. Further, the respective communication interfaces 512, 516and 524 may be adapted to transmit the credibility information togetherwith the determined piece of product price information and/or thecaptured data signal to a server over e.g. network 513, e.g. to anotherserver in the cloud computing environment. Thus, step 106 may beperformed at any of devices 500, 514 and 520.

Further, in any of the devices 500, 514 and 520, a step of capturing, bya microphone, voice data such as audio data including informationrelated to a price of a product, to a retailer of the product and/or toa type of the product may take place. Further, metadata including thegeographic position of the capturing and the time of capturing may bedetermined. Still further, the piece of product price informationincluding the type of the product, the price of the product and thepoint of retail may be determined by analyzing the captured data signal.The piece of product price information and the metadata may then beincluded into the database, e.g. located in the cloud computingenvironment.

Further, a server 526 may be included in the cloud computing environmentand may be accessible e.g. via network 513. The server may include acommunication interface 527 adapted to receive, e.g. from one of aplurality of mobile devices 500, 514, 520 and/or from a furthercomponent of the cloud, input data including information related to atype of a product (e.g. fuel), a price of the product, and point ofretail, the input data further including metadata, the metadataincluding a time of capturing of a data signal from which data signalthe information is derived, and a geographic position of the capturingof the data signal. Further, the server 526 may include a storageaccessor 528 adapted to access local and/or remote storages 529, 530,wherein the storage accessor 528 is further adapted to transmit thepiece of product price information including the type of the product,the price of the product and the point of retail together with themetadata to the storages 529, 530.

The server 526 may further include a processor 532 adapted to determinethe piece of product price information by analyzing the informationand/or the data signal. Thus, method step 104 may be performed at server526, as well as on any other server included in the cloud computingenvironment. Providing analyzing capacities in the cloud computingenvironment allows keeping the devices 500, 514 and 520 lean. Thus,these devices may be equipped with very little possessing capabilities.

Processor 532 of server 526 may further be adapted to determine and/orto receive, via the communication interface, credibility data related tothe information. Thus, method step 106 may either be performed at any ofdevices 500, 514 and 520, and/or at server 526, or alternatively or inaddition at a further server e.g. specifically provided for this task inthe cloud computing environment.

Processor 532 of server 526 may further be adapted to determineaggregated and/or estimated product price information by aggregating,for at least one of a plurality of types of a product and/or for atleast one of a plurality of points of retail, pieces of product priceinformation and the respective credibility data. Processor 532 mayfurther be adapted for estimating, for at least one of a plurality oftypes of the product and/or for at least one of a plurality of points ofretail, product price information and credibility information asdiscussed in the above. Further, processor 532 may be adapted to providethe aggregated and/or estimated product price information to storageaccessor 528 for transmission to storages 529, 530. Thus, method steps110 and 112 may be performed at server 526.

Further, processor 532 of server 526 may be adapted to estimatecredibility information of the aggregated and/or estimated product priceinformation, e.g. based on the credibility data received from at leastone mobile device 500, 514, 520 from which input data is received. Stillfurther, processor 532 may be adapted to provide the credibilityinformation to storage accessor 528 for transmission to storages 529,530. Thus, server 526, as well as another server in the cloudenvironment, may estimate the credibility information, which may beprovided, together with the aggregated or estimated price information,to a user upon a request.

Thus, as shown in FIG. 5a , a system for determining product priceinformation (e.g. fuel price information) may include a plurality ofmobile and stationary devices 500, 514 and 520 and at least one server526, wherein credibility data of the piece of product price informationis determined at one of the plurality of the devices 500, 514 and 520and/or at the server.

Still further, a computer program may include computer-programinstructions, which when executed by a computer cause the computer toperform any of the embodiments of the method described herein. For thispurpose, server 526 may provide a reading device 532 for reading anon-transitory computer-readable medium 534 including the computerprogram.

FIG. 5b is a hardware diagram of a processing system embodying aspectsof any of the devices 500, 514 and 520 and/or of server 526. Thus, thehardware diagram shows aspects which may be embodied in any of thedevices illustrated in FIG. 5 a.

The processes, algorithms and electronically driven systems described inthe following can be implemented via a discrete control device or acomputing system consistent with the structure shown in FIG. 5b . Such asystem is described herein as a processing system.

As shown in FIG. 5b , a processing system in accordance with thisdisclosure can be implemented using a microprocessor or its equivalent,such as a central processing unit (CPU) or at least one applicationspecific processor (ASP) (not shown). The microprocessor utilizes acomputer-readable storage medium, such as a memory (e.g., ROM, EPROM,EEPROM, flash memory, static memory, DRAM, SDRAM, and theirequivalents), configured to control the microprocessor to perform and/orcontrol the processes and systems of this disclosure. Other storagemediums can be controlled via a controller, such as a disk controller,which can control a hard disk drive or optical disk drive. Themicroprocessor or aspects thereof, in an alternate embodiment, caninclude or exclusively include a logic device for augmenting or fullyimplementing this disclosure. Such a logic device includes, but is notlimited to, an application-specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a generic-array of logic (GAL), andtheir equivalents. The microprocessor can be a separate device or asingle processing mechanism. Further, this disclosure can benefit fromparallel processing capabilities of a multi-core CPU.

In another aspect, results of processing of the input of data inaccordance with this disclosure can displayed via a display controllerto a monitor. The display controller would then preferably include atleast one graphic processing unit for improved computational efficiency.Additionally, an I/O (input/output) interface is provided for inputtingdata from a keyboard for a pointing device (not shown) for controllingparameters of the various processes and algorithms of this disclosurecan be connected to the I/O interface to provide additionalfunctionality and configuration options or control displaycharacteristics. Moreover, the monitor can be provided with atouch-sensitive interface to a command/instruction interface, and otherperipherals can be incorporated, including a scanner, a webcam, a stillimage camera, a video camera and/or a microphone as discussed in theabove.

The above-noted components can be coupled to a network, e.g. network513, and via network 513 to the cloud computing environment. Network 513may be the internet and/or a local intranet. Connection to the networkmay be achieved via network interface for the transmission or receptionof data, including controllable parameters.

The network 513 provides a communication path to the cloud computingenvironment, which can be provided by way of packets of data.Additionally, a central BUS is provided to connect the above hardwarecomponents together and provides at least one path for digitalcommunication therebetween.

Insofar as embodiments of the disclosure have been described as beingimplemented, at least in part, by a software-controlled data processingapparatus, it will be appreciated that the non-transitorymachine-readable medium carrying such software, such an optical disc, amagnetic disc, semiconductor or the like, is also considered torepresent an embodiment of the present disclosure.

FIG. 6 illustrates a community 600 of users contributing to and using anembodiment of the method for determining product price information asdiscussed in the above. Computing and storage resources are provided andassigned in a cloud environment 602 (cloud computing environment)including several servers and storage devices.

It is illustrated that plural devices may, at the same time, providedata signals including information related to a price of a product, to aretailer of the product and to a type of the product, or alternativelyor in addition provide a piece of product price information includingthe type of the product, the price of the product and the point ofretail, together with the metadata including the geographic position ofthe capturing and the time of the capturing. This information isprovided to cloud computing environment 602, e.g. via network 513.

At the same time, plural (e.g. other) users may request pricinginformation. Upon a corresponding request for product price informationreceived e.g. at the database held or distributed in cloud computingenvironment 602, the credibility information is transmitted togetherwith the product price information. In the example, a price P1 isindicated as having a credibility of 95%, while a price P2 is indicatedas having a credibility of 80%.

Thus, accurate pricing information may be gathered and distributed quasiin real-time, and may further be assigned with credibility informationallowing a user to estimate his own risk when driving to a point ofretail with the aim of buying the product at the estimated price, thatthe driving was not worth the effort and the fuel used for the drive hasbeen wasted since the estimated price was incorrect and/or is notavailable.

In FIG. 7, information related to a product consumption, such as fuelconsumption, is determined by analyzing the piece of product priceinformation and the metadata, or a plurality of pieces of product priceinformation together with the corresponding metadata. The informationrelated to product consumption may be displayed at a display 700 of adevice 702.

For example, the information related to product consumption may relateto fuel and may be displayed at a cockpit display 700 of a vehicle,wherein the underlying data signal may have been captured e.g. by thevehicle's front camera. For example, average values of productconsumption (fuel consumption) and of the price paid for the product(fuel) consumed by the vehicle may be derived and displayed.

By providing personal information related to product consumption, theuser of the capturing device may receive a reward for this contributionto the method and/or community. This may help to motivate a largepopulation of users to contribute to the method. Thus, product priceinformation may be acquired with a high frequency, resulting in a highaccuracy and/or reliability.

In FIG. 8, a further embodiment of the method of determining productprice information is illustrated.

At 800, a manual image recording is performed by a user using e.g. hissmartphone camera.

At 802, a continuous or automatic image recording is performed e.g. byan automotive front camera. For example, image recording may becontinuously performed when a vehicle is driving. Alternatively, theimage recording may be triggered when it is determined that the vehicleapproaches a vending centre such as a gas station, e.g. by evaluatingdata collected by a GPS (Global Positioning System) sensor.

At 804, a continuous or automatic voice recording is performed by a userusing e.g. his smartphone microphone. For example, the user may activatehis device (which may be in a stand-by mode) e.g. by uttering the voiceinput to be entered, thereby activating the device and entering theproduct price information by a single action. Further, the device mayalso be activated by any other means, e.g. by uttering a speech command(such as “listen”), by lifting or tilting the device (which by bedetermined using an acceleration sensor and/or a rate sensor) and/or bypressing a “dictation” button. Then, the user may conveniently enter thevoice input, e.g. including the product price information.

At 805, a keypad input is entered e.g. via the touchpad of a smartphone.

Any of the data signals captured at 800, 802, and 804 may includeinformation related to a price of a product (fuel, milk, butter, beer),to a retailer of the product and to a type of the product.

After the capturing, a tagging with metadata indicating e.g. ageographic position according to GPS coordinates or to a cellinformation of a cell-based communications network is included. Further,the data is tagged with a timestamp, indicating a time of capturing.

Processing steps 800 to 806 form a first phase of product priceinformation capturing and tagging. The corresponding processing stepsmay be performed by user devices, e.g. devices 500, 514 and 520.

At 808, the captured data is assigned to a specific retailer/point ofretail utilizing e.g. a database including retailers such as e.g. fuelstations and their geographic location.

At 810, an image and/or voice pre-processing of the captured data signalis performed e.g. with respect to crop, contrast, sharpness, etc. Thus,a higher quality of the data signal is achieved, and a highercredibility of the derived piece of product price information can beobtained.

At 812, a trust score is derived for the obtained price information. Thetrust score may be determined e.g. as illustrated in FIG. 2 by function200.

At 814, an (overall) product price estimation based on the derived trustscores is carried out. The product price estimation may include anaggregation or estimation as illustrated in FIG. 3.

Processing steps 808 to 814 form a second phase of processing and dataextraction. These steps can either be performed locally, e.g. at devices500, 514 and 520, or on a remote server, e.g. server 526. Further, theseprocess steps can also be performed by any other device forming part ofthe cloud computing environment.

For any communication needed between components of the cloud computingenvironment, wireless broadband communication techniques such asUMTS/LTE or WLAN may be applied.

At 816, a database is updated with the estimated or aggregated productprices and corresponding trust scores.

At 818, product prices, metadata, recommendations and past data isoffered to consumers, e.g. to the users having contributed to thegathering of pieces of product price information (e.g. for free), or tothe public, possibly as a commercial service (to be paid for).

Steps 816 and 818 form the back end service, allowing the users to usethe collectively gathered product price information.

Obviously, numerous modifications and variations of the presentdisclosure are possible in light of the above teachings. It is thereforeto be understood that within the scope of the appended claims, thedisclosure may be practiced otherwise than as specifically describedherein.

The present application claims priority to European Patent Application12 007 850.6, filed in the European Patent Office on Nov. 21, 2012, theentire contents of which being incorporated herein by reference.

The invention claimed is:
 1. A method for determining product priceinformation, including capturing a data signal including image data,voice data and/or keypad input data, the captured data signal includinginformation related to a price of a product, to a point of retail of theproduct and to a type of the product; determining metadata including ageographic position of the capturing and a time of the capturing;determining a piece of product price information including the type ofthe product, the price of the product, and the point of retail byanalyzing the captured data signal; determining a probability ofcorrectness of the piece of product price information as credibilitydata of the piece of product price information, the probability ofcorrectness being based at least in part on signal quality indicatingwhether noise has degraded the captured data signal and furtherdepending on personal credibility data of a user of a device at whichthe data signal has been captured, the personal credibility data beingderived from at least one of credibility data of information previouslyprovided by the user and information about personal credibility of theuser provided by other users; and including the piece of product priceinformation, the metadata and the respective credibility data into adatabase.
 2. The method of claim 1, wherein the credibility data furtherdepends on at least one of a matching of a color included in the imagedata with a color used by the retailer for marking his points of retail,and a matching of a captured pattern included in the image data with apredefined pattern used by the retailer for marking his points ofretail.
 3. The method of claim 1, including aggregating, for at leastone of a plurality of types of product and/or for at least one of aplurality of points of retail, pieces of product price information andthe respective credibility data in the database.
 4. The method of claim3, including estimating, for at least one of the plurality of types ofproduct and/or for at least one of the plurality of points of retail,product price information and credibility information of the estimatedproduct price information based on a plurality of pieces of productprice information and respective credibility data.
 5. The method ofclaim 3, wherein upon a request for product price information receivedat the data base, the credibility information is transmitted togetherwith the product price information.
 6. The method of claim 3, whereinthe product is fuel.
 7. A mobile device, including a capturing deviceincluding a still image camera, a video recorder, a microphone and/or akeypad adapted to capture a data signal relating to at least one of aprice of a product, a type of the product, a point of retail, a documentof retail and voice information; and circuitry configured to determine atime of capturing; detect a geographic position at the time ofcapturing, determine a piece of product price information including thetype of the product, the price of the product, and the point of retailby analyzing the captured data signal; determine a probability ofcorrectness of the piece of product price information as credibilitydata of the piece of product price information, the probability ofcorrectness being based at least in part on signal quality indicatingwhether noise has degraded the captured data signal and furtherdepending on personal credibility data of a user of a device at whichthe data signal has been captured, the personal credibility data beingderived from at least one of credibility data of information previouslyprovided by the user and information about personal credibility of theuser provided by other users; and transmit, to a server over a network,the credibility data and at least one of the captured data signaltogether with metadata including the time of capturing and thegeographic position of capturing and a piece of product priceinformation derived from the captured data signal together with themetadata.
 8. A server, including circuitry configured to receive inputdata including information related to a type of a product, to a price ofthe product, and to a point of retail, the input data further includingmetadata, the metadata including a time of capturing of a data signalfrom which data signal the information is derived, and a geographicposition of the capturing of the data signal; access a local and/orremote storage, further adapted to transmit a piece of product priceinformation including the type of the product, the price of the product,and the point of retail together with the metadata to the storage;determine the piece of product price information by analyzing theinformation and/or the data signal; and determine, and/or to receive viathe communication interface, a probability of correctness of the pieceof product price information as credibility data related to theinformation, the probability of correctness being based at least in parton signal quality indicating whether noise has degraded the captureddata signal and further depending on personal credibility data of a userof a device at which the data signal has been captured, the personalcredibility data being derived from at least one of credibility data ofinformation previously provided by the user and information aboutpersonal credibility of the user provided by other users.
 9. The serverof claim 8, wherein the circuitry is further configured to determineaggregated product price information by aggregating, for at least one ofa plurality of types of the product and/or for at least one of aplurality of points of retail, pieces of product price information andthe respective credibility data, and to provide the aggregated productprice information to the storage accessor for transmission to thestorage.
 10. The server of claim 8, wherein the circuitry is furtherconfigured to determine aggregated credibility information byaggregating, for at least one of a plurality of mobile devices fromwhich input data is received, the credibility data of the informationincluded in the input data, and further to provide the aggregatedcredibility information as personal credibility information to thestorage accessor for transmission to the storage.
 11. A non-transitorycomputer-readable medium storing a computer program includingcomputer-program instructions, which when executed by a computer, causethe computer to perform a method comprising capturing a data signalincluding image data, voice data and/or keypad input data, the captureddata signal including information related to a price of a product, to apoint of retail of the product and a type of the product; determiningmetadata including a geographic position of the capturing and a time ofthe capturing; determining a piece of product price informationincluding the type of the product, the price of the product, and thepoint of retail by analyzing the captured data signal; determining aprobability of correctness of the piece of product price information ascredibility data of the piece of product price information, theprobability of correctness being based at least in part on signalquality indicating whether noise has degraded the captured data signaland further depending on personal credibility data of a user of a deviceat which the data signal has been captured, the personal credibilitydata being derived from at least one of credibility data of informationpreviously provided by the user and information about personalcredibility of the user provided by other users; and including the pieceof product price information, the metadata and the respectivecredibility information into a database.
 12. A method for determiningproduct price information, including capturing, by a microphone, a datasignal including voice data including information related to a price ofa product, to a point of retail of the product and/or a type of theproduct; determining metadata including a geographic position of thecapturing and a time of the capturing; determining a piece of productprice information including the type of the product, the price of theproduct, and the point of retail by analyzing the captured data signal;determining a probability of correctness of the piece of product priceinformation as credibility data of the piece of product priceinformation, the probability of correctness being based at least in parton signal quality indicating whether noise has degraded the captureddata signal and further depending on at least one of personalcredibility data of a user of a device at which the data signal has beencaptured, the personal credibility data being derived from at least oneof credibility data of information previously provided by the user andinformation about personal credibility of the user provided by otherusers; and including the piece of product price information, thecredibility data, and the metadata into a data-base.
 13. The method ofclaim 1, including determining information related to productconsumption by analyzing the piece of product price information and themetadata; and displaying the information related to product consumptionat a device having captured the data signal and/or the voice data. 14.The method of claim 12, including determining information related toproduct consumption by analyzing the piece of product price informationand the metadata; and displaying the information related to productconsumption at a device having captured the data signal and/or the voicedata.